Application of virtual screening compound in preparation of NLRP3 inflammasome inhibitor

Abstract

The invention provides application of a virtual screening compound in preparation of an NLRP3 inflammasome inhibitor, and relates to the technical field of medicines.The application is characterized in that NLRP3 protein is used as a target point, and LRamp is adopted; an RF-ECFP4 integrated machine learning model is used for carrying out preliminary screening on a ZINC compound database, and nine small molecule compounds with high binding affinity are finally obtained by combining molecular docking, clustering analysis, in-vitro activity detection and ADMET property prediction. Experimental results show that the compounds can be stably combined with NLRP3 protein key residues, can significantly inhibit expression of inflammatory factors IL-1beta, IL-18 and TNF-alpha in an in-vitro level, and show good drug-likeness, oral absorption characteristic and safety. The compounds can be used for preparing drugs for treating NLRP3 inflammasome abnormal activation related diseases, and important lead compounds are provided for developing novel anti-inflammatory drugs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medicine, in particular to the application of a virtual screening compound in the preparation of an NLRP3 inflammasome inhibitor. BACKGROUND

[0002] Inflammasome is the "signal hub" of the body's natural immune system, and is essential for the clearance of disease-related damage, necrosis and functional damage cells. It has been found that there are many reported inflammasomes, among which the NOD-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome can be activated by a broad spectrum of pathogen-associated molecular patterns and damage-associated molecular patterns, and is the most in-depth inflammasome studied. As a multi-protein complex, NLRP3 inflammasome is composed of NLRP3, apoptosis-associated speck-like protein (ASC) and caspase-1. Activated NLRP3 inflammasome activates caspase-1, promotes the maturation and secretion of interleukin (IL-1β) and IL-18, and participates in the mediation of inflammatory response; by cleaving Gasdermin family proteins, it mediates inflammatory programmed cell death-pyroptosis, and thus becomes the "core commander" of innate immunity (Nat Rev Immunol. 2019, 19(8): 477-489; Cell. 2021, 184(26): 6299-6312. e22.). Activation of NLRP3 inflammasome is involved in oxidative stress, vascular endothelial dysfunction, inflammatory response, β-amyloid aggregation and other pathological processes, and plays an important role in the occurrence and progression of inflammatory diseases, central nervous system degenerative diseases and cardiovascular and cerebrovascular diseases (Cell Mol Biol Lett. 2023, 28(1): 51; Neuropharmacology. 2024, 252: 109941). Since NLRP3 is located upstream of inflammatory factors such as IL-1β / IL-18, targeting its activity can achieve the effect of "removing the root of evil", thereby effectively blocking the self-strengthening mechanism of chronic inflammation, providing an important strategy for the treatment of inflammation-related diseases. Inhibition of the activation of NLRP3 inflammasome can play a "four ounces move a thousand catties" in the treatment of many diseases, indicating its crucial position.

[0003] There are many existing NLRP3 inflammasome inhibitors, some of which directly target the NLRP3 protein such as MCC950, which has shown good efficacy in animal models of various diseases such as autoimmune diseases, cardiovascular diseases, cancers, nervous system diseases, diabetes, etc. There are also other components targeting NLRP3 inflammasome (such as endogenous small molecule inhibitor BHB) and downstream products (such as monoclonal anti-IL-1β antibody canakinumab). However, there are many limitations in the clinical application of NLRP3 inhibitors. Studies have shown that the pharmacokinetics and toxicokinetics of MCC950 limit its clinical application (Pharmacol Rev. 2021; 73(3): 968-1000). Although BHB can prevent chronic progressive diseases such as diabetes and AD, it has poor therapeutic effect in the face of acute inflammatory diseases (Nat Med. 2015; 21(3): 263-269). Canakinumab does not strongly inhibit NLRP3 inflammasome and shows inhibition of multiple inflammasomes (Front Immunol. 2023, 13: 1109938). Therefore, the development of new small molecule NLRP3 inhibitors with high efficiency, high specificity and good safety has become an urgent need for current research.

[0004] The rise of computer-aided drug design (CADD), especially high-throughput virtual screening technology, provides a powerful tool for accelerating the discovery of lead compounds. Precise virtual screening methods can effectively narrow the range of candidate compounds, have the advantages of low cost and high efficiency, and speed up the drug development process. Although traditional virtual screening has achieved certain results, it is still limited by high false positive rate, low screening efficiency and other problems, and it is urgent to use more advanced computing tools to quickly and accurately identify ideal lead compounds from a large number of compounds. By combining the high-throughput prediction ability of machine learning with the binding mode analysis ability of molecular docking technology, lead compounds with high affinity to the target and excellent pharmacokinetic properties can be more effectively identified, which is of great significance for promoting the development process of NLRP3 inhibitors. SUMMARY

[0005] Therefore, the present application proposes a virtual screening compound for preparing an NLRP3 inflammasome inhibitor, which selects NLRP3 protein as a target to screen compounds that can significantly inhibit the activation of NLRP3 inflammasome, providing a new idea and candidate molecules for preparing NLRP3 inhibitors.

[0006] To achieve the above-mentioned purpose, the present application provides a virtual screening compound for preparing an NLRP3 inflammasome inhibitor, which is any of the following compounds: .

[0007] The present application also provides a pharmaceutical composition comprising a therapeutically effective amount of the compound as described above, or a pharmaceutically acceptable salt, solvate or prodrug thereof, and one or more pharmaceutically acceptable carriers or excipients.

[0008] Further, the pharmaceutical composition is prepared into an oral preparation.

[0009] Further, the pharmaceutical composition is prepared into an injection preparation.

[0010] The present application also provides the use of the compound as described above or the pharmaceutical composition as described above in the preparation of a medicament for inhibiting the activity of NLRP3 inflammasome.

[0011] The present application also provides the use of the compound as described above or the pharmaceutical composition as described above in the preparation of a medicament for treating and / or preventing a disease mediated by abnormal activation of NLRP3 inflammasome.

[0012] Further, the disease is an autoinflammatory disease, a neurodegenerative disease, a cardiovascular and cerebrovascular disease, a liver disease, an inflammatory bowel disease or an arthritic disease.

[0013] Further, the autoinflammatory disease is selected from cryopyrin-associated periodic syndromes or familial Mediterranean fever, the neurodegenerative disease is selected from Alzheimer's disease, Parkinson's disease or Huntington's disease, the cardiovascular and cerebrovascular disease is selected from stroke, myocardial infarction or atherosclerosis, the liver disease is selected from nonalcoholic steatohepatitis or cirrhosis, the inflammatory bowel disease is selected from ulcerative colitis or Crohn's disease, and the arthritic disease is selected from rheumatoid arthritis or gout.

[0014] The present application also provides a method for inhibiting the activity of NLRP3 inflammasome in vitro, by applying the compound as described above or the pharmaceutical composition as described above to a macrophage culture system pre-stimulated by LPS and then activated by ATP, nigericin or MSU, and evaluating the inhibitory effect of the compound or the pharmaceutical composition on the activity of NLRP3 inflammasome by measuring the levels of IL-1β or IL-18 in the supernatant, the activation of caspase-1 or the formation of ASC speck.

[0015] The present application also provides a combination of the compound as described above or the pharmaceutical composition as described above in combination with another anti-inflammatory drug or a known NLRP3 inhibitor for the preparation of a pharmaceutical composition for combined use or for the use of combined administration.

[0016] Compared with the prior art, the present application has the beneficial effects that, The present application successfully identifies a series of novel NLRP3 inhibitor lead compounds from a large-scale compound library through an advanced virtual screening strategy combining machine learning and molecular docking. Compared with the prior art, the compounds and screening method provided by the present application exhibit significant technical progress and outstanding technical effects.

[0017] The candidate compounds obtained by the present application exhibit extremely high binding affinity to the NLRP3 target protein, and the molecular docking binding energy is significantly better than the conventional screening standard, indicating that they can strongly and stably bind to the key active site of the NLRP3 protein, effectively block the assembly and activation of the NLRP3 inflammasome at the molecular level by occupying its hydrophobic pocket and forming various intermolecular interactions with key amino acid residues, thereby laying a solid foundation for inhibiting the downstream inflammatory signaling pathway.

[0018] More importantly, these compounds not only have excellent in vitro activity potential, but also exhibit good drugability prospects through comprehensive ADMET property prediction. They all meet the drug-like rules and generally have good oral absorption characteristics and ideal metabolic stability. At the same time, a plurality of compounds exhibit low toxicity risk and acceptable safety characteristics, overcoming the clinical application limitations of some existing NLRP3 inhibitors in pharmacokinetics and toxicity, providing higher success probability for subsequent development.

[0019] In summary, the compounds provided by the present application provide valuable candidate molecules for developing new drugs for treating NLRP3 inflammasome-related diseases. These molecules have high activity and high drugability, making them exhibit broad application prospects and great development value in treating various major chronic inflammatory diseases such as autoimmune diseases, neurodegenerative diseases, and cardiovascular and cerebrovascular diseases. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 It is a schematic diagram of the three-dimensional structure of the NACHT domain of the NLRP3 protein, showing the active site region defined in virtual screening.

[0021] Figure 2 It is a schematic diagram of the overall flow of virtual screening, showing the screening steps from database construction to final candidate compound determination.

[0022] Figure 3 It is a molecular docking interaction mode diagram of 9 candidate compounds and NLRP3 protein.

[0023] Figure 4 It is a column chart of statistical results of the binding energy of 9 candidate compounds.

[0024] Figure 5A comprehensive radar plot of ADMET property prediction results for 9 candidate compounds. DETAILED DESCRIPTION

[0025] Various exemplary embodiments of the present application will now be described in detail, with reference to the drawings. The detailed description, which should be considered in conjunction with the accompanying drawings, is merely illustrative of certain aspects of the application and is not intended to limit the application beyond the limits of the appended claims.

[0026] All the raw materials of the present application are not particularly limited in origin, and can be purchased on the market or prepared according to conventional methods well known to those skilled in the art.

[0027] All the raw materials of the present application are not particularly limited in purity, and the present application preferably uses analytically pure raw materials or raw materials commonly used in the field of chemical synthesis.

[0028] Example 1 Construction of compound database and screening of machine learning model The SMILES code and IC 50 data information of the compounds were collected from the ChEMBL database by searching the inhibitor dataset with "human" and "NLRP3" as keywords. A total of 359 active compounds were screened out with "IC 50 ≤ 1 μmol / L" as the standard. 3×10 5 compounds were randomly downloaded from PubChem (https: / / www.ncbi.nlm.nih.gov / ) and 5×10 4The 1500 cluster center compounds were screened as non-active compound data set by K-means clustering algorithm according to the ratio of 1:4. Three kinds of molecular descriptors, including extended-connectivity fingerprint with diameter 4 (ECFP4), Molecular ACCess System Fingerprints (MACCS), and RDKit molecular fingerprint (RDK), were used to represent the compounds. Six basic machine learning models, including deep neural network (DNN), random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF), were established, and 18 basic machine learning models were constructed by combining the two. Eighteen kinds of integrated machine learning models were generated by random two-by-two combination, and the specific results are shown in Table 1. The optimal model was LR&RF-ECFP4.

[0029] Table 1 Evaluation parameters of integrated machine learning models

[0030] Example 2 Screening and clustering analysis of potential NLRP3 inhibitors The ZINC database 7.5×10 5 The 1500 cluster center compounds were screened as non-active compound data set by K-means clustering algorithm according to the ratio of 1:4. Three kinds of molecular descriptors, including extended-connectivity fingerprint with diameter 4 (ECFP4), Molecular ACCess System Fingerprints (MACCS), and RDKit molecular fingerprint (RDK), were used to represent the compounds. Six basic machine learning models, including deep neural network (DNN), random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF), were established, and 18 basic machine learning models were constructed by combining the two. Eighteen kinds of integrated machine learning models were generated by random two-by-two combination, and the specific results are shown in Table 1. The optimal model was LR&RF-ECFP4.

[0031] Table 2 Clustering analysis results of predicted active compounds

[0032] Example 3 Determination of activity of potential NLRP3 inhibitors The activity of the NLRP3 inhibitors obtained by screening was studied using molecular docking technology. First, the three-dimensional crystal structure of the NLRP3 protein (PDB ID: 8WSM; resolution: 2.70 Å) was downloaded from the RCSB PDB database. The structure was pre-processed, including: deleting water molecules, removing redundant chains, retaining the active domain, and performing hydrogen addition and charge optimization. Referring to the binding region of the co-crystal ligand, the active site coordinates were defined as (X = 15.362, Y = -12.741, Z = 23.501), and the docking cavity with a radius of 30 Å was set, thereby constructing the NLRP3 molecular docking model for virtual screening (structure schematic diagram, see Figure 1 ). Second, the structural formula of the small molecule compound to be docked was converted into an SDF file. After obtaining the binding pose and interaction of 20 candidate molecules and the protein NLRP3 using Autodock Vina 1.2.2, the binding energy generated by the interaction was counted, and the binding of the two was visualized and analyzed. The specific scores of the top 9 candidate compounds with lower binding energy scores are shown in Table 3 and Figure 3 . The mode of binding is shown in Figure 4 .

[0033] Table 3 Molecular docking binding energy of 9 candidate compounds

[0034] The chemical structural formula of the compound is as follows, Compound 1

[0035] ZINC number: ZINC7614893 Compound 2

[0036] ZINC number: ZINC1625364105 Compound 3

[0037] ZINC number: ZINC279819074 Compound 4

[0038] ZINC number: ZINC792459715 Compound 5

[0039] ZINC number: ZINC484859905 Compound 6

[0040] ZINC number: ZINC4585521 Compound 7

[0041] ZINC Number: ZINC1141764778 Compound 8

[0042] ZINC Number: ZINC66741513 Compound 9

[0043] ZINC Number: ZINC1587611020 Example 4 In vitro activity analysis of candidate compounds RAW264.7 macrophages (1.0*10 6 cells) were seeded in 6-well plates, and then DMEM containing LPS (100 ng / mL) and 9 candidate compounds (10 μM) was added for incubation for 12 h, and then ATP (5 mM) was added for co-incubation for 30 min. The cell supernatant was collected into 2.0 ml EP tubes, centrifuged at 12000 rpm for 5 min, and then the supernatant was collected into a new EP tube. The content of inflammatory factors in the cell supernatant was detected using an IL-1β, IL-18, and TNF-α commercial kit. The specific data are shown in Table 4. Table 4 Effect of 9 candidate compounds on inflammatory factors at an in vitro level

[0044] Example 5 ADMET property analysis of candidate compounds ADMETlab2.0 was used to predict the pharmacokinetics and toxicity of 9 compounds, including oral absorption (HIA), blood-brain barrier permeability (BBB), cardiac toxicity risk (hERG), liver toxicity, and the like. The results show that all the compounds have good oral absorption rates, and most of the compounds show low to moderate hERG risk, indicating that they have good drug development potential. The comprehensive prediction results are shown in Table 5. The comprehensive radar chart is shown in Figure 5 , wherein compounds 3 and 8 perform best in multiple pharmacokinetic parameters.

[0045] Table 5 ADMET prediction results of 9 candidate compounds

[0046] Example 6 Drug application prospect of compounds The 9 candidate compounds finally screened by the application can be stably combined with NLRP3 protein through the above-mentioned mechanism, and have good drug-like properties and pharmacokinetic characteristics, indicating that they have significant further development potential.

[0047] The compounds and pharmaceutically acceptable salts, solvates or prodrugs thereof can be combined with pharmaceutically acceptable carriers or excipients to prepare oral preparations, injections or other conventional pharmaceutical dosage forms for the preparation of drugs for treating or preventing diseases mediated by abnormal activation of NLRP3 inflammasome.

[0048] The diseases mediated by abnormal activation of NLRP3 inflammasome include but are not limited to diabetic nephropathy, Alzheimer's disease, stroke, inflammatory bowel disease, gouty arthritis and certain malignant tumors.

[0049] The above-described embodiments are only to describe the preferred modes of the present application, and are not intended to limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by those skilled in the art shall fall within the protection scope defined by the claims of the present application.

Claims

1. A virtual screening compound for preparing NLRP3 inflammasome inhibitors, characterized in that, It is any of the following compounds: 。 2. A pharmaceutical composition, characterized in that, It comprises a therapeutically effective amount of the compound as described in claim 1, or a pharmaceutically acceptable salt, solvate, or prodrug thereof, and one or more pharmaceutically acceptable carriers or excipients.

3. The pharmaceutical composition according to claim 2, characterized in that, The pharmaceutical composition is prepared into an oral formulation.

4. The pharmaceutical composition according to claim 2, characterized in that, The pharmaceutical composition is prepared as an injectable formulation.

5. The use of the compound as described in claim 1 or the pharmaceutical composition as described in any one of claims 2-4 in the preparation of a medicament, characterized in that, The drug is used to inhibit the activity of the NLRP3 inflammasome.

6. The use of the compound as described in claim 1 or the pharmaceutical composition as described in any one of claims 2-4 in the preparation of a medicament, characterized in that, The drug is used to treat and / or prevent diseases mediated by abnormal activation of the NLRP3 inflammasome.

7. The use according to claim 6, characterized in that, The diseases mentioned are autoinflammatory diseases, neurodegenerative diseases, cardiovascular and cerebrovascular diseases, liver diseases, inflammatory bowel diseases, or arthritis-related diseases.

8. The use according to claim 7, characterized in that, The autoinflammatory disease is selected from cold pyrrolizine-associated periodic syndrome or familial Mediterranean fever; the neurodegenerative disease is selected from Alzheimer's disease, Parkinson's disease or Huntington's disease; the cardiovascular and cerebrovascular disease is selected from stroke, myocardial infarction or atherosclerosis; the liver disease is selected from non-alcoholic steatohepatitis or cirrhosis; the inflammatory bowel disease is selected from ulcerative colitis or Crohn's disease; and the arthritis is selected from rheumatoid arthritis or gout.

9. A method for inhibiting NLRP3 inflammasome activity in vitro, characterized in that, The compound of claim 1 or the pharmaceutical composition of any one of claims 2-4 is applied to a macrophage culture system pre-stimulated with LPS and subsequently activated with ATP, nigericin or MSU. The method is used to evaluate the inhibitory effect of the compound or the pharmaceutical composition on NLRP3 inflammasome activity by measuring the levels of IL-1β or IL-18 in the supernatant, the activation of caspase-1 or the formation of ASC speck.

10. A combination of the compound of claim 1 or the pharmaceutical composition of any one of claims 2-4 with another anti-inflammatory drug or a known NLRP3 inhibitor, for the preparation of a pharmaceutical composition for combination therapy or for use in combination administration.

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